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Design of a Condition Monitoring System for Wind Turbines

Author

Listed:
  • Jinje Park

    (Department of Electrical Engineering, Changwon National University, Changwon 51140, Korea)

  • Changhyun Kim

    (Department of Electrical Engineering, Changwon National University, Changwon 51140, Korea)

  • Minh-Chau Dinh

    (Institute of Mechatronics, Changwon National University, Changwon 51140, Korea)

  • Minwon Park

    (Department of Electrical Engineering, Changwon National University, Changwon 51140, Korea)

Abstract

Renewable energy is being adopted worldwide, and the proportion of offshore wind turbines is increasing. Offshore wind turbines operate in harsh weather conditions, resulting in various failures and high maintenance costs. In this paper, a condition diagnosis model for condition monitoring of an offshore wind turbine has been developed. The generator, main bearing, pitch system, and yaw system were selected as components subject to the condition monitoring by considering the failure rate and downtime of the wind turbine. The condition diagnosis model works by comparing real-time and predictive operating data of the wind turbine, and about four years of Supervisory Control and Data Acquisition (SCADA) data from a 2 MW wind turbine was used to develop the model. A deep neural network and an artificial neural network were used as machine learning to predict the operational data in the condition diagnosis model, and a confusion matrix was used to measure the accuracy of the failure determination. As a result of the condition monitoring derived by inputting SCADA data to the designed system, it was possible to maintain the failure determination accuracy of more than 90%. The proposed condition monitoring system will be effectively utilized for the maintenance of wind turbines.

Suggested Citation

  • Jinje Park & Changhyun Kim & Minh-Chau Dinh & Minwon Park, 2022. "Design of a Condition Monitoring System for Wind Turbines," Energies, MDPI, vol. 15(2), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:2:p:464-:d:721302
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    References listed on IDEAS

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    2. Kim, Younghwan & Kim, Minki & Kim, Wonjoon, 2013. "Effect of the Fukushima nuclear disaster on global public acceptance of nuclear energy," Energy Policy, Elsevier, vol. 61(C), pages 822-828.
    3. Lacal Arantegui, Roberto & Jäger-Waldau, Arnulf, 2018. "Photovoltaics and wind status in the European Union after the Paris Agreement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2460-2471.
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    Cited by:

    1. Changhyun Kim & Minh-Chau Dinh & Hae-Jin Sung & Kyong-Hwan Kim & Jeong-Ho Choi & Lukas Graber & In-Keun Yu & Minwon Park, 2022. "Design, Implementation, and Evaluation of an Output Prediction Model of the 10 MW Floating Offshore Wind Turbine for a Digital Twin," Energies, MDPI, vol. 15(17), pages 1-16, August.
    2. Junshuai Yan & Yongqian Liu & Xiaoying Ren & Li Li, 2023. "Wind Turbine Gearbox Condition Monitoring Using Hybrid Attentions and Spatio-Temporal BiConvLSTM Network," Energies, MDPI, vol. 16(19), pages 1-22, September.

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